code: def get_ames_data():
      try:
          housing = fetch_openml(
              name="house_prices", as_frame=True, parser="auto"
          )
      except:
          housing = fetch_openml(name="house_prices", as_frame=True)
  
      housing_target = housing["target"].values
      housing_data_numeric = (
          housing["data"]
          .select_dtypes("number")
          .drop(columns=["Id"])
          .dropna(axis=1)
      )
      feature_names = housing_data_numeric.columns.values
      (
          X_train_reg,
          X_test_reg,
          y_train_reg,
          y_test_reg,
      ) = train_test_split(
          housing_data_numeric.values, housing_target, test_size=0.75
      )
      return (
          X_train_reg,
          X_test_reg,
          y_train_reg,
          y_test_reg,
          feature_names,
      )
  
file_path: imodels/notebooks/demo_helper.py
imports: import numpy
  from scipy.io.arff import loadarff
  from sklearn import metrics
  import matplotlib.pyplot
  from sklearn.datasets import fetch_openml
  from sklearn.model_selection import train_test_split
  import os
label_desc: Use image node_id 2 for calling a global function with example usage: get_ames_data() and returns: X_train_reg,  X_test_reg,  y_train_reg,  y_test_reg,  feature_names